Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
We see an interesting spread with an outlier to the right. Answer the following questions, please:
Using log-transform allow us to fit a linear regression rather than a polynomial to the data. Also it deals with outliers, as illustrated below, including the outlier, but NOT log-transforming, would make it difficult to get a sense of the data visually.
gapminder %>%
filter(year==1952) %>%
ggplot(aes(gdpPercap, lifeExp, size = pop))+
geom_point()
gapminder %>%
filter(year == 1952) %>%
slice(which.max(gdpPercap))
## # A tibble: 1 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Kuwait Asia 1952 55.6 160000 108382.
From executing the chunk above we see that Kuwait is the outlier with a GDP in 1952 of 108.382.
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Tasks:
options("scipen" = 100) # removing scientific notation
gapminder %>%
filter(year == 2007) %>%
ggplot(aes(gdpPercap, lifeExp, size=pop, color=continent))+
geom_point()+
scale_x_log10()+
ggtitle("Life Expectancy and GDP in 2007", subtitle = "With point size determined by population size and color by continent")+
xlab("GDP per Capita")+
ylab("Life Expectancy (in years)")+
labs(color="Continent", size="Population size")+
theme(
plot.title = element_text(hjust = 0.5, size = 18),
plot.subtitle = element_text(hjust = 0.5, size = 11, face="italic"))
gapminder %>%
filter(year == 2007) %>%
slice_max(gdpPercap, n=5) %>%
select(country, gdpPercap)
## # A tibble: 5 × 2
## country gdpPercap
## <fct> <dbl>
## 1 Norway 49357.
## 2 Kuwait 47307.
## 3 Singapore 47143.
## 4 United States 42952.
## 5 Ireland 40676.
From executing the chunk above we see that Norway, Kuwait, Singapore, The United States and Ireland are the top 5 richest countries in 2007.
The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
…
This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.
…
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages
Can you add a title to one or both of the animations above that will change in sync with the animation? (Hint: search labeling for transition_states() and transition_time() functions respectively)
Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.
gapminder %>%
ggplot(aes(gdpPercap, lifeExp, size=pop, color=continent))+
geom_point()+
scale_x_log10()+
transition_time(year)+
labs(title = 'Development of Life Expectancy and GDP',
subtitle = "Year: {frame_time}",
x = "GDP per capita",
y = "Life Expectancy",
color="Continent",
size="Population size")
gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]Question:
I want to see whether there’s a correlation between CO2-emissions and the wealth of a country (as measured by GDP). I do this by downloading a dataset containing CO2 emission from the gapminder website and combining with the gapminder data we’ve been working with so far.
# loading a csv showing CO2 emissions pr person
df_emission <- read_csv("co2_emissions_tonnes_per_person.csv")
## Rows: 194 Columns: 220
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): country, 1830, 1831, 1832, 1833
## dbl (215): 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
df_emission_2007 <- df_emission %>%
select(country, `2018`) %>%
rename(Emission=`2018`)
gapminder_2007 <- gapminder %>%
filter(year==2007)
df_emission_2007_full <- merge(df_emission_2007, gapminder_2007)
head(df_emission_2007_full)
## country Emission continent year lifeExp pop gdpPercap
## 1 Afghanistan 0.254 Asia 2007 43.828 31889923 974.5803
## 2 Albania 1.590 Europe 2007 76.423 3600523 5937.0295
## 3 Algeria 3.690 Africa 2007 72.301 33333216 6223.3675
## 4 Angola 1.120 Africa 2007 42.731 12420476 4797.2313
## 5 Argentina 4.410 Americas 2007 75.320 40301927 12779.3796
## 6 Australia 16.900 Oceania 2007 81.235 20434176 34435.3674
df_emission_2007_full %>%
ggplot(aes(Emission, gdpPercap, color=continent))+
geom_point()+
scale_x_log10()+
labs(
title="CO2 Emission and GDP in 2007",
x="CO2 emission pr. person (log scale)",
y="GDP per capita",
color="Continent")+
theme(plot.title = element_text(hjust = 0.5, size = 18))
Answer:
The graph does indeed seem to suggest that our general CO2 emission pr. person increases as the wealth of a country grows. The graph also suggests that generally European countries omit more CO2 pr. person than people from African countries, and interestingly enough also seem to omit more CO2 than people in American countries.